The deficiencies in previous research on failure prediction studies were identified from the international literature. This study’s purpose is to address these deficiencies while using a new method in developing failure prediction models, namely recursive partitioning (specifically the classification tree algorithm).The deficiencies were addressed as follows:Brute empirism was avoided by focussing on cash flow ratios in combination with certain accrual ratios. Failure was not only defined as bankruptcy, but as any condition where the company cannot exist in future in its current form, therefore including delistings as well as major structural changes. By using the population of listed industrial companies between June 1997 and May 2002, the grey area in-between ‘successful’ and ‘bankrupt’ was included in developing the models. Every model developed was tested with the help of an independent sample. The different economic cycles were considered by developing different models for a growth and a recessionary period. A combined model was also developed, with the economic cycle as a independent dichotomous variable.When the prediction accuracy for the different classes and in total, of the models developed, is compared with the ex ante probability that an observation will fall in a particular class of the majority (non-failed companies), the prediction accuracy is in every instance higher than the ex ante probability.
This article assesses the state of cash flow reporting by listed South African industrial companies in order to evaluate whether the users of financial statements can accept them as being reliable and use them as a tool to compare the operating performance of various companies. As the cash flow statement has been in use since 1989, it was envisaged that compliance would be high. However, it was found that there are several companies that deviate from some of the requirements of AC 118 regarding cash flow statements.
In this article, modifications are suggested for the current format of the cash flow statement, which is prescribed by AC 118, in order to address ambiguities and improve comparability. This redefinition of activities, together with the alteration of the layout, leads to a better explanation of the cash‐generating function of an enterprise. The authors argue that the separation of the cash flow for the maintenance of the existing resource base and the cash flow for the expansion thereof, is essential information in a model for the prediction of the future cash flow generation of a company. The resultant increase in the accessibility, reliability and utility of cash flow reporting should enhance users’ economic decision making and liberalise financial information. The modifications proposed in the article can therefore assist standard setters to improve financial reporting.
Cash is king. Even a highly profitable company can find itself in search of financing due to a lack of cash to honour its obligations. If this situation is only temporary and external sources of finance are freely available, this cash flow obstacle does not have to be detrimental to the stakeholders of the company.However, if the poor cash position of a company is not temporary, but rather an integral part of its structure and a result of its strategy, stakeholder interest may be at risk. Although insolvency is seldom the outcome, such companies find themselves struggling because of their cash flow inflexibility.The cumulative index-difference aims to identify companies that are cash flow inflexible, in order to enable stakeholders to take timely measures to prevent a negative outcome. With adjustments in strategy and preventative measures taken, the cash flow positions can be improved to prevent a disaster.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.